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© 2016 Asian Economic and Social Society. All rights reserved

ISSN (P): 2304-1455/ISSN (E):2224-4433

Volume 6(12), 240-253

240

EMPIRICAL EVIDENCE ON CONTRACT FARMING IN NORTHERN

NIGERIA: CASE STUDY OF TOMATO PRODUCTION

Iro Ibrahim Kutawa

College of Economics & Management, South China Agricultural University, Guangzhou 510642,

China

Article History:

Received: 25-Jan-2017

Revised received: 20-

Feb-2017

Accepted: 25-Feb-2017

Online available: 10-

Mar-2017

Keywords: Tomato income,

Nigeria,

Participation,

Transaction cost,

Household income

Abstract

This study contributes to the scarce empirical evidence on contract

farming in Northern Nigeria using a case study of tomato

production. Using data from five Local Government Areas of Kano

State in Northern Nigeria, a total of 116 tomato contract farmers

and 84 non contract farmers were selected. The econometric result

indicated that there was a high level of participation in contract

farming. Participation in contract farming generated desirable

causal effects on transaction costs, productivity, tomato income,

total household income and poverty status of the farmers and this

implies that the contract farming arrangement is very appealing to

the farmers currently engaged in contract farming. The major

factors that swayed the farmers’ decision to engage in contract

farming were education, farm size and extension indicating that

these variables are key policy variables that could be leveraged to

influence participation in contract farming in the study area. The

study may give detailed information on how contract and non-

contract tomato production is currently functioning in northern

Nigeria.

1. INTRODUCTION1

Contract Farming (CF) is defined as “an agreement between farmers and processing and/or

marketing firms for the production and supply of agricultural products under forward agreements,

frequently at pre-determined prices” (Eaton and Sherperd, 2011). CF refers to an agreement on

agricultural production vis-à-vis buyers and farmers that institutes settings for the production and

selling of farmhouse produce. Generally, the farmers agree to deliver certain quantities of a

specific product at the quantified eminence criteria and time, and the buyer might also supply

some inputs or hands-on backing to the farmer. In Southern and Eastern Africa, CF is

synonymously referred to as “Out grower Scheme” and can be used for several products, even

though in middle income economies, it is typical for staple crops like yams, plantain and rice.

Corresponding author’s

Email address: [email protected]

Asian Journal of Agriculture and Rural Development

http://www.aessweb.com/journals/5005

DOI: 10.18488/journal.1005/2016.6.12/1005.12.240.253

mailto:[email protected]

http://www.aessweb.com/journals/5005

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Asian Journal of Agriculture and Rural Development, 6(12)2016: 240-253

241

Empirical studies in middle income economies give mixed investigations about the safety and

membership outcome of CF. Although the degree at which membership subsidizes to the well-

being of smallholders endure a practical question, several authors found that partaking increases

farmers’ earnings (Barrett et al., 2012; Bellemare, 2012; Warning and Key, 2002). Singh (2002)

was of the view of the exclusion of smallholders from partaking in CF. In this prospective, many

researchers recommended the inclusion of smallholders in CF Warning and Key (2002) and

Miyata et al. (2009). The literature similarly posits numerous complications startling CF

performance, which include: deferred payments, cheating, high defaulting rate, biased terms and

with no reimbursement for crop failure (Guo, 2005; Singh, 2002). Barrett et al. (2012) re-counted

cases of high input turn over as both parties fail to honor their agreements. The double-hurdle

model was used to standardize the drivers of engrossment in CF. It is a parametric synopsis of the

Tobit model, in which two isolated stochastic processes regulate the certainty to partake in CF

(Greene, 2007). Pronouncements in the Tobit model, participation and the level or extent of

participation are presumed to be jointly and henceforth the factors affecting the two level

decisions are equivalent. However, the choice to partake might lead to the conclusion

level/intensity of participation and therefore the control variables may differ (Asfaw et al., 2011).

The double-hurdle model is applied in a way that, both hurdles (the decision for input in CF and

the continuous participation) have equations associated with them, incorporating the outgrowth of

farmer's characteristics and circumstances. In estimating the double-hurdle model, a Probit

regression (with complete observations) is tracked by a condensed regression on the non-zero

observations (Cragg, 1971) and this was used to define the resolution to participate and the

level/intensity of involvement in contractual arrangements in Zambia (Kiwanuka and Machethe,

2016). The double-hurdle assumes that households make two sequential decisions with regard to

participating and level/intensity of a scheme like CF or the use of a technology. The number 1 is

assigned to the first decision variable (D) for farmers who participated in contract farming and the

value zero for otherwise. However, the expected utility of participating in CF (Di*) is latent. The

PSM (Propensity Score Matching) was used to resolve the concussion of input in CF transaction

costs and welfare. Basically, the PSM framework matches the observations of participants and

non-participants of CF, conferring to the anticipated susceptibility of input in CF (Wooldridge,

2005). The underlying principle of PSM is that the predicted probabilities (propensity scores) from

an estimated probit or logit model are used to find matches for farmers partaking in CF. The

overall objective of the study is the empirical evidence on contract farming in Northern Nigeria

using a case study of tomato production. The specific objectives are to:

i) Determine the basis of involvement into tomato CF

ii) Effect of partaking in CF on transaction cost

iii) Influence of participation in CF on Output, Earnings and Wellbeing

2. METHODOLOGY

2.1. Sampling technique

A pre-survey was conducted to identify the tomato farmers under contract and those under the

conventional farming system so as to establish a complete sampling frame and afterwards, a pilot

survey was conducted to pre-test the questionnaire in order to help detect any fault that may

surface in the questionnaire administration sample designs. The target populations were tomato

farmers in the villages where tomato is mostly grown in the thirty villages of five local

governments targeted in the study area, the population of tomato producers amounted to 2,143

farmers. Multi-stage sampling technique was employed. In the second stage, five local

government areas, namely: Garun Mallam, Kura, Bunkure, Rano and Kiru were randomly selected

and the third stage involved the purposive choice of six villages from each of the local government

areas.


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2.2. Data collection Using a survey method encompassing a designed questionnaire, primary data were collected from

farmers. The socioeconomic data collected included sex of the respondent, cropping pattern,

household size, age, marital status and formal education levels. Production information collected

included size of farmland owned, size of land under tomato production, type of labour used in

production, varieties of seed planted, fertilizer application, cyclical yields and domestic income.

Amount of credit, access to extension services were also among production information (number

of visits), amount of fertilizers used. Market information was also collected which included prices

of seeds, seasonal quantities produced, cost and returns, produce sold. Data about constraints

faced by tomato farmers and suggestions to increase their output was also collected.

2.3. Double-hurdle model

This model outline the impact of drivers in CF. It is a parametric simplification of the Tobit

model, in which dual distinct stochastic processes define the resolution and level to partake in CF

(Greene, 2007). Decisions and level or extent of membership in the Tobit model, are supposed to

be the same. Nonetheless, Asfaw et al. (2011), suggested that the proclamation to partake may

lead the level/intensity of participation decision and therefore the control variables in both stages

may vary. In this model, both hurdles (the decision for partaking in CF and the level of

participation) have equations associated with them, integrating the accouterments of the farmer's

physiognomies and surroundings. In estimating the double-hurdle model, a Probit regression

(utilizing complete observations) is tracked by a condensed regression on the non-zero

observations (Cragg, 1971) and this determine the resolution to partake and the level/intensity of

involvement in contractual arrangements in Zambia (Kiwanuka and Machethe, 2016). The double-

hurdle assumes that households make two sequential decisions for participating and level/intensity

of contribution in a scheme like CF or the use of machinery. Each hurdle is habituated by the

family circle socioeconomic characteristics and institutional variables. However, a diverse

underlying variable is used in the double-hurdle model, to epitomize each resolution procedure.

The first decision variable (D) is 1 for farmers who have partook in CF and zero for otherwise.

The expected utility of participating in CF (Di*) is latent however.

Evaluated with a Probit model, the first hurdle input equation is given as:

𝐷𝑖∗ = 𝑎𝑍𝑖 + 𝑢𝑖 … … … … … … … … … … (1)

𝐷𝑖 = 1 𝑖𝑓 𝐷𝑖∗ > 1 𝐷𝑖 = 0 𝑖𝑓 𝐷𝑖

∗ ≤ 1

Where,

𝐷𝑖∗ = 1 if the farmer participates in tomato CF and 0 otherwise,

𝑍𝑖 = descriptive vector variables (farmer/farm specific characteristics and institutional

characteristics that influences the likelihood of partaking in CF),

𝑎= vector of parameter estimates,

𝑢𝑖 = error term.

The second hurdle of double-hurdle model involves an outcome equation, which uses a truncated

model that determines the level of participation in CF measured in terms of the proportion of farm

area allocated to tomato CF relative to the cumulative cultivable crop area owned. Therefore, the

second hurdle uses observations only from those farmers who indicated a positive value on

partaking in CF. It is worth stating that the farmers’ involvement in CF does not partake at the

same level of participation. Hence, the level/intensity of participation (level of participation

hurdle) of in tomato contract farming is projected using a Tobit-like model given as:

𝑌𝑖∗ = 𝛽𝑋𝑖 + 𝑣𝑖 … … … … … … … … … … … … (2)


243

𝑌𝑖 = {𝑌𝑖

∗

0

𝑖𝑓 𝑌𝑖∗ > 0 𝑎𝑛𝑑 𝐷∗ > 0

𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒

Where,

𝑌𝑖 = observed response on the proportion of land allocated to tomato contract farming,

𝑋𝑖 = vector of explanatory variables,

𝛽 = vector of parameter estimates,

𝑣𝑖 = error term

The observed value of the proportion of land allocated to tomato contract farming is therefore

given by:

𝑌𝑖 = 𝐷𝑖𝑌𝑖∗ … … … … … … … … … … … … (3)

The error terms of the two decision models (participation model and level of participation model)

are distributed as follows:

{𝑢𝑖

𝑣𝑖

~ 𝑁(0, 1)

~ 𝑁(0, 𝜎2) … … … … … … … … … … … … (4)

The error terms 𝑢𝑖 and 𝑣𝑖 are usually assumed to be independently and normally distributed. It is

assumed that for each respondent the decision whether to participate in contract farming and the

decision about the participation level are made independently. The two decision processes are

non-separable and thus both parts of the likelihood function must be maximized simultaneously.

Moffat (2005) was of the view that a variable appearing in both equations of the double-hurdle

model have reverse effects.

2.4. Propensity score matching (PSM)

PSM was used to evaluate the impact of membership in contract farming transaction costs and

welfare. PSM technique is a non-parametric approach that involves constructing a statistical

comparison group by modeling the probability of participating in contract farming/adopting a

technology on the basis of practical features that are unpretentious by the contract

farming/technology. The underlying principle of PSM is that the predicted probabilities

(propensity scores) from an estimated probit or logit model are used to find matches for farmers

participating in contract farming (participants).

The estimation of average treated effect on the treated (ATT) is specified as follows:

𝐴𝑇𝑇 = 𝐸{𝐻1|𝐷 = 1} − 𝐸{𝐻0|𝐷 = 1} … … … … … … … … … … … … (5)

The problem with estimation of the equation (6) is that 𝐸{𝐻0|𝐷 = 1} is not observable. However,

it is probable to appraise equation (6) by replacing 𝐸{𝐻0|𝐷 = 1} with 𝐸{𝐻0|𝐷 = 0} as follows

𝐴𝑇𝑇 = 𝐸{𝐻1|𝐷 = 1} − 𝐸{𝐻0|𝐷 = 0} … … … … … … … … … … … … (6)

Valuation of equation (7) is a biased estimate of the causal effect of membership in CF. This leads

to the modeling of a more reliable estimation by controlling observable characteristics (𝑍) to

ensure that participation in CF is random and not connected with the outcome variables i.e

restricted independence hypothesis is satisfied.

𝑃 (𝑍) = 𝑃𝑟{𝐷 = 1|𝑍} = 𝐸{𝐷|𝑍} … … … … … … … … … … … … (7)


244

𝐴𝑇𝑇 = 𝐸{𝐻1 − 𝐻0|𝐷 = 1} … … … … … … … … … … … … (8)

𝐴𝑇𝑇 = 𝐸{𝐸{𝐻1 − 𝐻0|𝐷 = 1, 𝑃(𝑍)}} … … … … … … … … … … … … (9)

𝐴𝑇𝑇 = 𝐸{𝐸{𝐻1|𝐷 = 1, 𝑃(𝑍)}} − 𝐸{𝐻0|𝐷 = 0, 𝑃(𝑍)}|𝐷 = 0} … … … … … … … … … … . … (10)

Where,

𝐻1 = value of the outcome for participants in tomato contract farming,

𝐻0 = value of the outcome for adopters of the new technology,

𝐷 = Participation (1 for participants in tomato contract farming and 0 otherwise),

𝑍 = vector of explanatory variables.

This study employed two matching techniques (Nearest Neighbor Matching and Kernel Based

Matching) instead of only one to ensure robustness of the impact of farmers’ involvement in CF.

3. RESULTS AND DISCUSSION

3.1. Descriptive results of the continues variables

The result given in Table 1 shows that a larger proportion of the contract farmers (54.3%) and

non-contract farmers (50%) respectively had no proper education. This matches cordially with

the findings of Ayandiji (2011) who reported that 57% of the tomato farmers in Ogun State,

Nigeria had no official education. This finding implies that the majority of the farmers are not

favourably disposed to the influence of education on their farm production activities due to

their lack of education. This is in accordance with the literature that education builds a

supportive mental attitude for getting innovative practices, particularly information and

management-intensive practices. Thus, the more educated the farmer is, the higher the

likelihood of participating in CF as they are in a position to acknowledge the benefits and

advantages of partaking. According to Beard (2005), the exceptional educated household

head; is likely to participate in projects.

Table 1: Distribution of tomato farmers based on educational qualification

Educational qualification Contract farmers Non-contract farmers

Frequency Percentage Frequency Percentage

No formal education 63 54.3 42 50

Primary education 18 15.5 17 20.2

Secondary education 25 21.6 13 15.5

Tertiary education 10 8.6 12 14.3

Total 116 100 84 100

The results in Table 2 below shows that 98% of the non-contract farmers cultivated tomatoes in

0.1-1.0 hectares of farmland compared to 93% of the contract farmers. On the average, the

contract farmers cultivated tomatoes on 1.0 hectares compared to 0.8 hectares for the non-

contract farmers, signifying that tomato farming by both the contract and non-contract farmers

were on a small scale. This result is in support of relates Maliwichi et al. (2014) who reported that

65% of the tomato farmers in Limpopo province, South Africa had farm size of at most 2 hectares.

Thus the production of tomato on a small scale could be attributed to low access to large

agricultural land which makes agricultural productivity growth through farm area expansion

limited and therefore, productivity growth through sustainable intensification is a better option.

This further validates the important role of CF in driving sustainable tomato production in the

study area.


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Table 2: Distribution of tomato farmers based on farm size

Farm size Contract farmers Non-contract farmers

(Hectares) Frequency Percentage Frequency Percentage

0.1 – 1.0 108 93.1 82 97.6

1.1 – 2.0 8 6.9 2 2.4

Total 116 100.0 84 100.0

Mean 1.0

0.8

The result in Table 3 shows that most (97%) of the contract farmers had contacts with extension

agents compared to 56% of the non-contract farmers. This result is expected as CF arrangement

gives the contract farmers more access to agricultural extension support services as one of the

benefits of the contractual agreement. This is not in tandem with Usman and Bakari (2013) who

reported that a majority (64%) of the tomato farmers in Adamawa state, Nigeria had no access to

extension. With CF, access to extension service is mostly made accessible to the contract farmers

depending on the terms of the contract.

Table 3: Distribution of tomato farmers based on their frequency of extension contact

Extension Contract farmers Non-contract farmers

contact Frequency Percentage Frequency Percentage

No contact 4 3.4 37 44.0

Had contact 112 96.6 47 56.0

Total 116 100.0 84 100.0

3.2. Level of participation in tomato contract farming

Mwambi et al. (2016) pointed out that the concept of smallholder farmers’ participation in CF is

fundamental for policy makers pursuing to uphold rural economic activity and development. This

makes understanding of tomato farmers’ level of participation in contract farming in the study

area a critical issue. The result presented in Table 4 indicates that a majority (60%) of the tomato

contract farmers had high participation in contract farming in terms of allocating a larger

proportion of their cultivable land to tomato contract farming and this implies that contract

farming arrangement is very appealing to the farmers currently engaged in contract farming.

Also, the result shows that there were farmers with very low, low and very high level of

participation in tomato contract farming. This stipulates the level of disparity in membership in the

study area of contract farming.

Table 4: Level of participation of farmers in tomato contract farming

Level of participation Frequency Percentage

Very low (1 – 25%) 15 12.9

Low (26 – 50%) 21 18.1

High (51 – 75%) 69 59.5

Very high (76 – 100%) 11 9.5

Total 116 100.0

NB: Level of participation in contract farming was defined based on percentage of land allocated to tomato

contract farming in relation to the total land cultivated by the farmers

3.3 Determinants of participation in tomato contract farming

Table 5 presents the result of the estimated Probit model of the determinants of participation in

tomato contract farming. The Likelihood ratio (LR) of 152.03 of the estimated Probit model for

tomato contract farming is significant at 1% probability level and this implies the joint

significance of the explanatory variables included in the models. There is disparity in the factors

that leverage the farmers’ involvement in tomato CF. The coefficient for education was positive

and significant in influencing farmer’s participation in tomato contract farming at 1% level of


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probability. This result is in support of Arumugam et al. (2011) whose findings indicated that

education was positive and significantly influenced the farmers’ likel ihood of participation in

fresh fruits and vegetable farming in Malaysia. Coefficient for farm size was positive and

significant in influencing farmer’s participation in tomato contract farming at 1% level of

probability and this shows a direct relationship between farm size and participation in tomato

contract farming. This result disagrees with the findings of Azumah et al. (2016) whose study

showed a negative relationship between farm size and participation in contract farming in

Northern Ghana. Coefficient for extension contact was positive and significant in influencing

farmer’s participation in tomato contract farming at 1% level of probability. This result is in

accordance with that of Anim (2011) who recounted that in the Limpopo province of South

Africa, extension visits was absolutely important in swaying the maize farmers’ input in CF

3.4. Level of participation in tomato contract farming

The result of the estimated truncated regression models in Table 5 shows that LR of 183.23 of the

fitted models for data generated from tomato contract farming was significant at 1%. This

indicates the joint significance of the explanatory variables in influencing the level or intensity of

participation in tomato contract farming. The results revealed that there is some variation in the

results of the estimated probit model and truncated regression models and this implies that the

factors that influenced the farmers’ decision to participate in tomato contract farming were not

exactly the same factors that influenced the farmers’ intensity of participation in tomato contract

farming. This further justifies the use of double-hurdle model in examining farmer’s participation

in tomato contract farming in the study area as against the use of Tobit regression.

Table 5: Double-hurdle estimates of determinants of participation in tomato CF

Variables First hurdle: Probit model Second hurdle: Truncated model

Intercept 4.335*** (2.94) 1.579** (2.20)

Gender 0.382 (1.32) 0.171 (1.54)

Age -0.149** (-1.99) -0.193 (-1.12)

Education 0.133*** (2.28) 0.027** (2.19)

Family size -0.923 (-1.02) 0.017* (1.85)

Farm size 0.138*** (2.78) 0.062*** (2.41)

Farming exp 0.384* (1.77) -0.049 (-0.37)

Farm assets 1.117 (0.89) 0.394* (1.76)

Access to credit 1.158** (3.79) 0.081 (1.60)

Extension contact 1. 750*** (2.50) 0.019*** (2.92)

Association 1.294*** (2.43) 0.315 (0.94)

Input subsidy -0.283 (-0.66) -0.055 (-1.04)

Market distance 0.443 (1.34) -0.084 (-0.97)

Observations 200 116

LR chi2 152.03 183.23

Prob> chi2 0.00 0.00

Log likelihood -58.95 -82.12

Sigma 1.656 (2.97*)

Pseudo R2 0.56

NB: values in parenthesis are the t values

***, **, * implies significant at 1%, 5% and 10% probability levels respectively

The estimated coefficient of education was positive and significant in influencing the farmer’s

level of participation in tomato contract farming at 5% probability level, implying that more

educated farmers had a higher probability of increasing their level of participation in contract

farming. This outcome supports the findings of Kiwanuka and Machethe (2016). Kiwanuka and

Machethe (2016) result showed that education positively influenced the level/intensity of

participation in the interlocked contractual arrangement for dairy in Zambia. Also, Tongchure,


247

and Hoang (2013) found that a household members’ level of education positively influenced

farmers’ likelihood to participate in contract participation in Thailand, noting that farmers who

complete higher education would find it easier to understand the information given when

receiving advice from the extension agents. The estimated coefficient of farm size was positive

and significant in influencing the farmer’s level of participation in tomato contract farming at 1%

probability level. This is expected because the increasing level of participation in contract farming

would mean increasing the land area allocated to tomato production. This means that as farm size

accessible of farmers’ increases, the probability of increasing their level of participation increases.

This result is identical with the findings of other scholars who observed the direct relationship

between increased levels of commercialization and increased land size. The estimated coefficient

of extension contact was positive and significant in influencing farmer’s level of participation in

tomato contract farming at 10% probability level. This implies that farmers who are very much in

contact with extension agents have more tendency of increasing their level of participation in

contract farming. In addition, farmers with increased access to extension will be more informed on

challenges and can upgrade their know-how on developmental projects or schemes (Sidibé, 2005).

3.5. Impact of participation in contract farming on transaction costs nexus

The two main assumptions underlying the consistency of propensity score matching techniques

were evaluated based on results in Table 6 and 7 before proceeding to establish the causal effect of

participation in contract farming on transaction costs. The balancing test is usually vital after

matching to examine whether the alterations in covariates of both groups (contract farmers and

non-contract farmers) in the coordinated sample have been excluded, in which case, the accorded

corresponding group can be accorded a reliable counter to fact. The results of the simulation-based

sensitivity analysis for PSM estimates as a test for the robustness of ATT for failure of the CIA as

put forward by Ichino et al. (2008) is obtainable in Table 2. The results show that the matching

method of the propensity score yields robust estimates of the ATT because the estimates with

binary cofounder differ by less than 9% from the standard matching estimate since the simulated

ATT exceeded 8.35% of the ATT baseline. This implies that the ATT is enthusiastic to potential

deviations from the CIA and the potential confounder does not epitomize a real threat for the

robustness of the standard estimate.

Table 6: PSM balancing properties of covariates before and after matching

Matching

algorithm

Outcome

indicators

Pseudo R2

BM AM

LR P-value

BM AM

Mean absolute bias

BM AM

Absolute bias

reduction

NNM Transaction cost 0.38 0.06 0.001 0.158 31.10 10.65 65.8

KBM Transaction cost 0.35 0.05 0.001 0.167 29.84 7.11 76.2

NB: BM = before matching, AM = after matching, LR = likelihood ratio

Table 7: PSM estimates simulation-based sensitivity analysis

Outcome variable

Neutral confounder Confounder calibrated to mimic

access to credit

Estimate

effecta

Outcome

effectb

Selection

effectc

Estimator

effecta

Outcome

effectb

Selection

effectc

Transaction costs (N) 1.02% 0.98 2.33 8.35% 1.61 2.10 a The estimator effect indicates to what extent the baseline estimation result would change if we could

observe an additional binary confounder. b The outcome effect measures the estimated effect of the simulated binary confounder on the outcome

variable-transaction costs. c The selection effect measures the estimated effect of the simulated binary confounder on the selection into

treatment-the propensity of participation in tomato contract farming.

The results of the mean treatment effects on the treated ATT appraised by both NNM and KBM

matching methods are presented in Table 8. The results indicate that the transaction costs


248

(packaging, handling, transportation, communication costs etc.) incurred by the tomato contract

farmers were significantly lower than that of the non-contract farmers with a mean difference of

N11429.62 and N10130.67 for NNM and KBM matching methods respectively at 1% probability

level. This implies that involvement in CF had an adverse effect on contract farmers by

significantly decreasing the burden of transaction costs in the purchase of inputs for tomato

production and also, disposal of the tomato output due to the contractual agreement. This result is

in consonance with Tatlidil and Aktur (2004) who revealed that the transaction cost (proxied by

transportation cost) of tomato contract farmers ($25.90) was lower than the non-contract farmers

($47.20) in the Bida district of Canakhale province, Turkey. Also, Birthal et al. (2008) reported

that CF significantly reduced the transaction costs of dairy contract farmers. Noting that

transaction costs were as high as 22 % of the total cost in the open market, and 9% under contract

farming in India. Osebeyo and Aye (2014) described that policies in Nigeria that are intended at

minimizing transaction costs will help in upholding tomato production, decreasing poverty among

rural households there by sustaining agricultural growth. The transaction costs, reducing effect of

tomato contract farming in the study area compare favourably with the findings of Setboonsarng et

al. (2006) who reported that their results offer credibility to the debate that CF can be an

operational institutional mechanism to ease transaction costs faced by small-scale rice farmers in

Thailand. Other studies (Swinnen and Maertens, 2007; Oya, 2012) have equally indicated that

contract farming is a veritable tool that can be explored for reducing transaction costs in food

value chains.

Table 8: PSM estimates of the impact of CF on transaction costs

Matching

algorithm Outcome indicator

Mean of outcome indicator ATT

CF NCF

NNM Transaction costs(N) 22750.50 34180.12 11429.62***(12.38)

KBM Transaction costs(N) 22880.10 33010.77 10130.67***(5.22)

NB: * P < 0.1, ** P < 0.05, *** P < 0.01

CF = contract farmers, NCF = non-contract farmers

3.6. Productivity, income and welfare gain of participation in contract farming

The results presented in Tables 9 and 10 were used to evaluate the balancing property and

conditional independence assumptions respectively. The balancing property was satisfied based on

the low pseudo-R2, insignificant p-values of the likelihood ratio test in comparison with the values

before and after matching, high total bias decrease and lower mean standardized bias detected

after matching in contrast with the values before matching. This implies that differences between

the groups (contract farmers and non-contract farmers) in observed factors that could explain both

selection into tomato contract farming as well as biased estimates for the outcome variables

(productivity, tomato income, total household income and poverty) are properly controlled before

the treatment effect valuation. To ensure there is substantial overlap in the distribution of the

propensity scores for contract farmers and non-contract farmers, the common support condition

was correspondingly enforced in the valuation process. As proposed by Heckman et al. (1997),

individual annotations in the common support region were used in the analysis (region where the

propensity score of the control units is greater than the minimum propensity score of the treated

units and the propensity score of the treated units is less than the maximum propensity score of the

control units).


249

Table 9: PSM balancing properties of covariates before and after matching

Matching

algorithm

Outcome

indicators

Pseudo R2 LR P-value Mean

absolute bias Absolute bias

reduction BM AM BM AM BM AM

NNM

Productivity

(Kg/ha) 0.462 0.032 0 0.301 21.54 2.33 81.6

Tomato income

(N) 0.311 0.045 0.001 0.271 28.11 7.43 73.6

Total household

income (N) 0.305 0.024 0.007 0.52 32.61 8.4 74.2

Poverty 0.49 0.011 0.004 0.281 25.33 5.64 77.7

KBM

Productivity

(Kg/ha) 0.295 0.026 0.003 0.33 23.17 7.45 67.8

Tomato income

(N) 0.431 0.012 0.001 0.461 29.01 9.14 68.5

Total household

income (N) 0.413 0.035 0.004 0.459 33.5 12.1 63.9

Poverty

(Headcount) 0.351 0.029 0.001 0.356 27.12 8.27 69.5

NB: BM = before matching, AM = after matching, LR = likelihood ratio

Table 10 presents the simulated-based sensitivity analysis result that reports robustness of

matching estimators to failure of CIA. This indicates that the propensity score matching technique

yields robust estimates of the ATT and therefore, the baseline ATT is robust to possible deviations

from the CIA.

The matching results from both NNM and KBM matching techniques in Table 11 revealed that

the contract farmers had significantly higher mean yield of 3.8 ton/ha and 3.7 ton/ha at 5%

probability level suggesting that contract farming had a positive impact on the productivity of the

farmers. This implies that participation in contract farming resulted in yield enhancing effect on

tomato production and this stems from increased access to timely inputs, improved production

technologies, credit, technical support and advisory services that contract farming guarantees the

contract farmers.

Table 10: PSM estimates simulation-based sensitivity analysis

Outcome variables

Neutral confounder Confounder calibrated to mimic

access to credit

Estimate

effecta

Outcome

effectb

Selection

effectc

Estimator

effecta

Outcome

effectb

Selection

effectc

Productivity (Kg/ha) 1.45% 1.34 1.68 4.51% 0.34 2.65

Tomato income (N) -0.33% 1.02 1.21 11.63% 1.05 1.98

Total household

income (N) 0.65% 1.59 1.33 2.11% 0.22 2.51

Poverty (Headcount) 2.81% 1.88 1.57 -3.78% 0.95 2.70 a The estimator effect indicates to what extent the baseline estimation result would change if we could

observe an additional binary confounder. b The outcome effect measures the estimated effect of the simulated binary confounder on the outcome

variables-productivity, tomato income, total household income and poverty. c The selection effect measures the estimated effect of the simulated binary confounder on the selection into

treatment-the propensity of participation in tomato contract farming.

The PSM results using NNM revealed that the ATT in tomato income and total household income

between the two groups was estimated at N 39879 and N 39993 respectively and was statistically


250

significant at 1% probability level. This indicates that participation in contract farming generated

positive income effects on the contract farmers which enable them to improve their standard of

living. This positive income effect is expected through increased productivity, higher producer

prices and reduced price risk. Also, the ATT in tomato income and total household income

between the two groups using KBM produced similar result. This finding agrees with Vande velde

and Maertens (2014) who reported that participation in contract-farming significantly increases

rice income by 181.8 Euros in Benin republic. Other studies that established positive income

effects of contract farming include (Cahyadi and Waibel, 2015; Wainaina et al., 2014; Sambuo,

2012; Saigenji and Zeller, 2009; Waswa et al., 2012; Sokchea and Culas, 2015). The positive

income effect of tomato contract farming disagrees with the findings of Abdulai and Al-hassan

(2016) who reported that contract farmers earn less from soybean cultivation as compared to their

non-contract counterparts in Eastern corridor of the Northern Region, Ghana noting that the major

reason for this outcome is because contractors apply more market power over the farmers. The

results of the estimated ATT by NNM as shown in Table 10 indicates that poverty (incidence of

poverty) among the tomato contract farmers (34%) was significantly lower than that of the non-

contract farmers (45%) with a mean difference of -0.11(-11%) which was statistically significant

at 5% probability level. Related result was also acquired using KBM. This result implies that

contract farming had a significant poverty reducing effect on the tomato farmers and therefore,

offers opportunity for alleviating poverty among tomato-based farmers in the study area. A

plausible explanation for the poverty reducing effect of contract farming arises from the multiple

production and marketing benefits that accrue to the tomato contact farmers as a result of the

contractual agreement which led to increased productivity, income generation and invariably,

reduced poverty incidence. The result of the poverty decreasing effect of CF is in line with

Adjognon and Naseem (2012) who opined that CF is a tool for poverty alleviation in Africa.

Table 11: PSM estimates of the impact of contract farming on productivity, income and

welfare

Matching

algorithm Outcome indicators

Mean of outcome

indicators ATT

CF NCF

NNM

Productivity (Kg/ha) 12510 8760 3750** (4.37)

Tomato income (N) 205550 165671 39879***(8.01)

Total household income (N) 491560 451567 39993***(1.89)

Poverty (headcount) 0.34 0.45 -0.11**(2.23)

KBM

Productivity (Kg/ha) 12895 9224 3671**(2.41)

Tomato income (N) 211455 184220 27235**(2.01)

Total household income (N) 473880 463100 10780NS

Poverty (headcount) 0.35 0.48 -0.13**(2.37)

NB: * P < 0.1, ** P < 0.05, *** P < 0.01, NS= not significant

CF = contract farmers, NCF = non-contract farmers

4. CONCLUSIONS

This study contributes to the scarce empirical evidence on contract farming in Northern Nigeria

using a case study of tomato production. The main factors that swayed the farmers’ decision to

partake in CF and level of involvement were education, farm size and extension implying that

these variables are key policy variables that could be leveraged to influence participation in

contract farming in the study area. As expected, there was high level of participation in contract

farming and this implies that contract farming arrangement is very appealing to the farmers

presently betrothed in it. In other words, these variables can be very instrumental in conditioning

farmers participation behavior with respect to contract farming provided they are properly

integrated in policy framework geared towards encouraging farmers’ participation in contract

farming. Participation generated desirable causal effects on transaction costs, productivity, tomato


251

income, total household income and poverty status of the farmers implying that with continued

participation in contract farming, farmers are assured of reduced transaction costs and increased

welfare gains. Thus, this research has contributed in supporting empirical evidence on contract

farming as a strategy for farmers to realize welfare gains from their production.

Recommendations

Based on the findings of the study, the following recommendations have been put forward:

i. In the light of delayed payment for crop produce which was indicated as a key challenge by

some of the contract farmers, there should be interest payment for delay in payment to

farmers as part of contractual agreement to curb the issue of intentional delay of payment by

the contracting firms.

ii. Appropriate measures should be put in place to ensure that barriers to inclusion of resource

poor farmers in contract farming can be readily addressed. This can be achieved through

farmer groups which gives poor farmers the opportunity of pooling their limited resources

as a group and linking up with contracting firms.

iii. To avoid default in meeting the terms of contract by both farmers and contractors,

appropriate policy framework should be put in place by government to support small scale

farmers involved in contract farming and also protect the interest of contractors.

iv. Increasing farmers’ access to land is a viable option for promoting participation in

contracting farming because farm size influenced both decision to partake.

Funding: Financial support from the China Scholarship Council and the Federal Government of Nigeria

Competing Interests: The author declares that s/he has no conflict of interests.

Contributors/Acknowledgement: All the designing and estimation of current research done by sole

author.

Views and opinions expressed in this study are the views and opinions of the authors, Asian Journal of

Agriculture and Rural Development shall not be responsible or answerable for any loss, damage or liability

etc. caused in relation to/arising out of the use of the content.

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